Triple

T8107929
Position Surface form Disambiguated ID Type / Status
Subject Choudens E189272 entity
Predicate publishedWork P80 FINISHED
Object Carmen
Carmen is a famous 1875 opera by Georges Bizet, renowned for its passionate music and tragic story of love, jealousy, and freedom set in Seville.
 E36362 NE FINISHED

How this triple was built (4 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Carmen | Statement: [Choudens, publishedWork, Carmen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Carmen
Context triple: [Choudens, publishedWork, Carmen]
  • A. Carmen
    Carmen is a key character in the dark fantasy film "Pan’s Labyrinth," serving as the pregnant mother whose fragile health and marriage to a brutal captain frame the story’s wartime and familial tensions.
  • B. Carmen
    Carmen is a central district of San José, Costa Rica, known for its urban character and role in the capital’s administrative and commercial life.
  • C. Carmen
    Carmen is a supporting character in Jim Jarmusch’s film "Broken Flowers," connected to the protagonist’s journey to revisit women from his past.
  • D. Carmen
    Carmen is a landlocked municipality in the central part of Bohol Island in the Philippines, known for its proximity to the famous Chocolate Hills.
  • E. Carmen
    Carmen is a pivotal character in the 1986 film "The Color of Money," serving as the savvy and manipulative girlfriend-manager of young pool hustler Vincent Lauria.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Carmen
Triple: [Choudens, publishedWork, Carmen]
Generated description
Carmen is a famous 1875 opera by Georges Bizet, renowned for its passionate music and tragic story of love, jealousy, and freedom set in Seville.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Carmen
Target entity description: Carmen is a famous 1875 opera by Georges Bizet, renowned for its passionate music and tragic story of love, jealousy, and freedom set in Seville.
  • A. Carmen chosen
    Carmen is a famous opera by Georges Bizet, renowned for its passionate music and tragic story centered on the free-spirited gypsy Carmen.
  • B. Carmen
    Carmen is a 1983 Spanish musical drama film directed by Carlos Saura that reimagines the classic Bizet opera through flamenco dance.
  • C. Carmen
    Carmen is a feminine given name of Latin origin, widely used in Spanish-speaking cultures and beyond.
  • D. Carmen
    Carmen is a landlocked municipality in the central part of Bohol Island in the Philippines, known for its proximity to the famous Chocolate Hills.
  • E. Carmen
    Carmen is a central district of San José, Costa Rica, known for its urban character and role in the capital’s administrative and commercial life.
  • F. None of above.

Provenance (5 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69ca82b9d5848190a24672775d5c5011 completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb42fa40e08190955fccec1a28eb34 completed March 31, 2026, 3:43 a.m.
NED1 Entity disambiguation (via context triple) batch_69cfc9129bec8190b3c1471ac8a131ac completed April 3, 2026, 2:05 p.m.
NEDg Description generation batch_69cfc9b8cd108190a9b3482858b0363a completed April 3, 2026, 2:07 p.m.
NED2 Entity disambiguation (via description) batch_69cfca54d4dc8190a2f94a6f3d2acaf9 completed April 3, 2026, 2:10 p.m.
Created at: March 30, 2026, 5:32 p.m.